scholarly journals Letter in response to Burke et al . (2020): Trends in opioid use disorder and overdose among opioid‐naive individuals receiving an opioid prescription in Massachusetts from 2011 to 2014

Addiction ◽  
2020 ◽  
Vol 115 (8) ◽  
pp. 1591-1593 ◽  
Author(s):  
Stefan G. Kertesz
Keyword(s):  
Opioid Use Disorder ◽  
Opioid Use ◽  
Opioid Prescription

Opioid use disorder and overdose among youth following an initial opioid prescription

Addiction ◽  
2021 ◽  
Author(s):  
Scott E. Hadland ◽  
Sarah M. Bagley ◽  
Mam Jarra Gai ◽  
Joel J. Earlywine ◽  
Samantha F. Schoenberger ◽  
...  
Keyword(s):  
Opioid Use Disorder ◽  
Opioid Use ◽  
Opioid Prescription

scholarly journals Stigma of Opioid Use Disorder and Its Indirect Effects on Student Pharmacists’ Perceptions and Attitudes

Pharmacy ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 144
Author(s):  
Elizabeth A. Hall ◽  
Alina Cernasev ◽  
Umida Nasritdinova ◽  
Michael P. Veve ◽  
Kenneth C. Hohmeier
Keyword(s):  
Community Pharmacy ◽  
Student Perceptions ◽  
Opioid Use Disorder ◽  
Vital Role ◽  
Primary Objective ◽  
Community Resources ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Electronic Survey ◽  
Perceptions And Attitudes

Objectives: Pharmacists play a vital role in serving patients during the ongoing nationwide opioid epidemic, and so it is also critical to educate the next generation of pharmacists on opioids and opioid use disorder (OUD). The primary objective of this study was to quantitatively characterize student perceptions of opioid use and the stigma associated with OUD. Secondary aims were to determine whether differences in perceptions exist based upon the student’s year in the Doctor of Pharmacy program or employment in a community pharmacy. Methods: First-, second-, third-, and fourth-year student pharmacists voluntarily completed an electronic survey regarding perceptions of opioid use and stigma associated with OUD. Results: Of the 9 survey items, students were most uncomfortable referring patients to community resources for addiction support and/or treatment (25.3% comfortable or very comfortable). Students working in a community pharmacy were significantly more comfortable talking to patients attempting to refill opioids early and providing opioid counseling as compared to their peers not working in community pharmacy. Fourth-year students reported a higher level of comfort talking to a patient attempting to refill an opioid prescription early, counseling a patient on an opioid prescription, and providing information about alternatives to opioids. Third-year students responded most favorably to the items regarding how well the curriculum has prepared them to interact with patients taking opioids and those with OUD. Conclusions: These findings reveal that students are comfortable counseling on opioids and discussing alternative options. Differences in perceptions were observed based upon the student’s year in the program and whether or not they were employed in a community pharmacy setting.

scholarly journals A Deep Learning Method to Detect Opioid Prescription and Opioid Use Disorder from Electronic Health Records

2021 ◽  
Author(s):  
Aditya Kashyap ◽  
Chris Callison-Burch ◽  
Mary Regina Boland
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
The United States ◽  
Opioid Use Disorder ◽  
Unstructured Data ◽  
Learning Approaches ◽  
Learning Models ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Patients At Risk

Objective: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. Materials and Methods: We developed an informatics algorithm that trains two deep learning models over patient EHRs using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both of these challenging outcomes. Results: Our deep learning models incorporate both structured and unstructured data elements from the EHRs to predict opioid prescription with an F1-score of 0.88 +/- 0.003 and an AUC-ROC of 0.93 +/- 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 +/- 0.05 and AUC-ROC of 0.94 +/- 0.008. Discussion: Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a.) deep learning approaches in predicting OUD and b.) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore this represents an important contribution of our work as opioid prescriptions are more common than OUDs. Conclusion: Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

scholarly journals LO91: Opioid poisoning and opioid use disorder in older trauma patients

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S40-S41
Author(s):  
R. Daoust ◽  
J. Paquet ◽  
L. Moore ◽  
A. Cournoyer ◽  
M. Emond ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorder ◽  
Proportional Hazards ◽  
Opioid Use Disorder ◽  
Cox Proportional Hazards ◽  
Trauma Patients ◽  
Opioid Use ◽  
Multicenter Cohort Study ◽  
Opioid Prescription ◽  
History Of

Introduction: Patients hospitalized following a trauma will be frequently treated with opioids during their stay and after discharge. We examined the relationship between acute phase (< 3 months) opioid use after discharge and the risk of opioid poisoning (OP) or opioid use disorder (OUD) in older trauma patients Methods: In a retrospective multicenter cohort study conducted on registry data, we included all patients aged 65 years and older admitted (hospital stay >2 days) for injury in 57 trauma centers in the province of Quebec (Canada) between 2004 and 2014. We searched for OP and OUD from ICD-9 and ICD-10 code diagnosis that resulted in a hospitalization or a medical consultation after their initial injury. Patients that filled an opioid prescription within a 3-month period after sustaining the trauma were compared to those who did not fill an opioid prescription during that period using Cox proportional hazards regressions. Results: A total of 70,314 participants were retained for analysis; median age was 82 years (IQR: 75-87), 68% were women, and 34% of the patients filled an opioid prescription within 3-months of the initial trauma. During a median follow-up of 2.6 years (IQR: 1-5), 192 participants (0.30%; 95%CI: 0.25%-0.35%) were hospitalized for OP and 73 (0.10%; 95%CI: 0.07%-0.13%) were diagnosed with OUD. Having filled an opioid prescription within 3-months of injury was associated with an increased hazard ratio of OP (2.6; 95%CI: 1.9-3.5) and OUD (4.0; 95%CI: 2.3-7.0). However, history of OP (2.7; 95%CI: 1.2-6.1), of substance use disorder (4.3; 95%CI: 2.4-7.9), or of opioid prescription filled (2.7; 95%CI: 2.1-3.5) before trauma were also related to OP or OUD. Conclusion: Opioid poisoning and opioid use disorder are rare events after hospitalization for trauma in older patients. However, opioids should be used cautiously in patients with history of substance use disorder, opioid poisoning or opioid use during the past year.

scholarly journals The Contribution of Prescribed and Illicit Opioids to Fatal Overdoses in Massachusetts, 2013-2015

2019 ◽  
Vol 134 (6) ◽  
pp. 667-674 ◽  
Author(s):  
Alexander Y. Walley ◽  
Dana Bernson ◽  
Marc R. Larochelle ◽  
Traci C. Green ◽  
Leonard Young ◽  
...  
Keyword(s):  
Prescription Drug ◽  
Drug Monitoring ◽  
Monitoring Program ◽  
Opioid Use Disorder ◽  
Medical Examiner ◽  
Prescription Opioids ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Prescription Drug Monitoring Program ◽  
Opioid Medications

Objectives: Opioid-related overdoses are commonly attributed to prescription opioids. We examined data on opioid-related overdose decedents in Massachusetts. For each decedent, we determined which opioid medications had been prescribed and dispensed and which opioids were detected in postmortem medical examiner toxicology specimens. Methods: Among opioid-related overdose decedents in Massachusetts during 2013-2015, we analyzed individually linked postmortem opioid toxicology reports and prescription drug monitoring program records to determine instances of overdose in which a decedent had a prescription active on the date of death for the opioid(s) detected in the toxicology report. We also calculated the proportion of overdoses for which prescribed opioid medications were not detected in decedents’ toxicology reports. Results: Of 2916 decedents with complete toxicology reports, 1789 (61.4%) had heroin and 1322 (45.3%) had fentanyl detected in postmortem toxicology reports. Of the 491 (16.8%) decedents with ≥1 opioid prescription active on the date of death, prescribed opioids were commonly not detected in toxicology reports, specifically: buprenorphine (56 of 97; 57.7%), oxycodone (93 of 176; 52.8%), and methadone prescribed for opioid use disorder (36 of 112; 32.1%). Only 39 (1.3%) decedents had an active prescription for each opioid detected in toxicology reports on the date of death. Conclusion: Linking overdose toxicology reports to prescription drug monitoring program records can help attribute overdoses to prescribed opioids, diverted prescription opioids, heroin, and illicitly made fentanyl.

Initiation of Long-Acting Opioids Following Hospital Discharge Among Medicare Beneficiaries

2021 ◽  
Author(s):  
Bhushan R Deshpande ◽  
Ellen P McCarthy ◽  
Yoojin Jung ◽  
Timothy S Anderson ◽  
Shoshana J Herzig
Keyword(s):  
Hospital Discharge ◽  
Hospice Care ◽  
Opioid Use Disorder ◽  
High Dose ◽  
Medicare Beneficiaries ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Short Acting ◽  
Long Acting

Guidelines recommend against initiating long-acting opioids during acute hospitalization, owing to higher risk of overdose and morbidity compared to short-acting opioid initiation. We investigated the incidence of long-acting opioid initiation following hospitalization in a retrospective cohort of Medicare beneficiaries with an acute care hospitalization in 2016 who were ≥65 years old, did not have cancer or hospice care, and had not filled an opioid prescription within the preceding 90 days. Among 258,193 hospitalizations, 47,945 (18.6%) were associated with a claim for a new opioid prescription in the week after hospital discharge: 817 (0.3%) with both short- and long-acting opioids, 125 (0.1%) with long-acting opioids only, and 47,003 (18.2%) with short-acting opioids only. Most long-acting opioid claims occurred in surgical patients (770 out of 942; 81.7%). Compared with beneficiaries prescribed short-acting opioids only, beneficiaries prescribed long-acting opioids were younger, had a higher prevalence of diseases of the musculoskeletal system and connective tissue, and had more known risk factors for opioid-related adverse events, including anxiety disorders, opioid use disorder, prior long-term high-dose opioid use, and benzodiazepine co-prescription. These findings may help target quality-improvement initiatives.

scholarly journals The opioid crisis: 18 years of opioid prescriptions in spine patients

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
P Calvachi ◽  
E Mezzalira ◽  
A Boaro ◽  
A Duey ◽  
F Bolivar ◽  
...  
Keyword(s):  
Spinal Surgery ◽  
Massachusetts General Hospital ◽  
Spinal Pain ◽  
Primary Diagnosis ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Prescribing Practices ◽  
Opioid Prescription ◽  
Prescription Rates ◽  
Pain Symptoms

Abstract Background The US opioid epidemic continues to afflict patients and the healthcare system. Surgery remains a risk factor for opioid misuse, and treatment of low back pain in orthopedics and neurosurgery is one of the largest introductions of opioids into the community. The objective of the study is to understand how opioid prescribing practices for spinal surgery have evolved in two academic hospital in the last 18 years. Methods Data were obtained from the Research Patient Data Registry for Brigham and Women's and Massachusetts General Hospital from January 2000 to December 2018. Patients included had a primary diagnosis of degenerative diseases, trauma, spinal infection, spinal deformities, or spinal pain symptoms/syndromes; were aged &gt; 18 years; and had an opioid prescription. Covariates included demographics, diagnoses, comorbidities, procedures, opioid type, number of prescriptions, route of administration, doses and length of prescription. Results A total of 38,250 patients with spine-related diagnoses received an opioid prescription. The median age was 63 years (18-107), 50% male and 86% white. A total of 32,304 patients (84.4%) received at least one opioid prescription during their hospitalization. The sum of opioid prescriptions filled (inpatient and outpatient) were 889,868 between 2000 and 2018 (55.2% oral, 41.7% intravenous). Oxycodone was the most prescribed. The dose of ≥ 50 morphine milligram equivalents MME/day was reduced from 65.0% in 2000 to 17.3% in 2018, and doses ≥ 90 MME/day dropped from 26.9% in 2000 to 6.4% in 2018. However, the duration of prescription has increased from 4.1% having an opioid prescription for &gt;7 days in 2000, to 21.7% in 2018. Conclusions Opioid prescription rates for spinal surgery patients have increased since 2000, declined temporarily in 2016, but are rising again. Physicians are prescribing fewer MMEs per day but have increased longitudinal dosing, which still leaves patients at risk for misuse and opioid use disorder. Key messages Between 2000 and 2016 there was an increase of 140 times the number of opioid prescriptions for spine patients. More interventions and non-pharmacological solutions are needed to reduce this public health epidemic.

Trends in opioid use disorder and overdose among opioid‐naive individuals receiving an opioid prescription in Massachusetts from 2011 to 2014

Addiction ◽  
2019 ◽  
Vol 115 (3) ◽  
pp. 493-504 ◽  
Author(s):  
Laura G. Burke ◽  
Xiner Zhou ◽  
Katherine L. Boyle ◽  
E. John Orav ◽  
Dana Bernson ◽  
...  
Keyword(s):  
Opioid Use Disorder ◽  
Opioid Use ◽  
Opioid Prescription

scholarly journals Development of a Natural Language Processing Machine to Enhance Identification of Opioid Use Disorder in Electronic Health Records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Avery Chadd ◽  
Rebecca Silvola ◽  
Yana Vorontsova ◽  
Andrea Broyles ◽  
Jonathan Cummins ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Health Records ◽  
Icd Codes ◽  
Electronic Health

Background/Objective: Real-world data, including electronic health records (EHRs), has shown tremendous utility in research relating to opioid use disorder (OUD). Traditional analysis of EHR data relies on explicit diagnostic codes and results in incomplete capture of cases and therefore underrepresentation of OUD rates. Machine learning can rectify this by surveying free clinical notes in addition to structured codes. This study aimed to address disparities between true OUD rates and cases identified using traditional ICD codes by developing a natural language processing (NLP) machine for identifying affected patients from EHRs. Methods: Patients (≥12 years old) who had received an opioid prescription from IU Health or Eskenazi Health between 1/1/2009 and 12/31/2015 were identified by the Regenstrief Institute. Exclusion criteria included any cancer, sickle cell anemia, or palliative care diagnoses. Cases of OUD were identified through ICD codes and NLP. The NLP machine was developed using a dictionary of key OUD terms and a training corpus of 300 patient notes. A testing corpus of 148 patient notes was constructed and validated by manual review. The NLP machine and ICD 9/10 codes were independently tested against this corpus. Results: Although ICD codes identified OUD cases with high specificity (98.08%), this method demonstrated moderate sensitivity (53.13%), accuracy (68.92%), and F1 score (68.92%). Testing using the NLP method demonstrated increased sensitivity (93.75%), increased accuracy (89.19%), and increased F1 score (91.84%); specificity mildly decreased (80.77%). Conclusion: Our revised NLP machine was more effective at capturing OUD cases in EHRs than traditional identification using ICD codes. This illustrates NLP’s enhanced capability of identifying OUD cases compared to structured data. Potential Impacts: These findings establish a role for NLP in OUD research involving large datasets. Ultimately, this is intended to improve identification of risk factors for OUD, which is of significant clinical importance during a public health crisis. 

scholarly journals Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects on brain

2021 ◽  
Author(s):  
Rachel L Kember ◽  
Rachel A. Vickers-Smith ◽  
Heng Xu ◽  
Sylvanus Toikumo ◽  
Maria Niarchou ◽  
...  
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Opioid Use Disorder ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Genome Wide ◽  
The Cross

Despite an estimated twin heritability of ~50%, genome-wide association studies (GWAS) of opioid use disorder (OUD) have revealed few genome-wide significant (GWS) loci, with replicated findings only in European-ancestry individuals. To identify novel loci, including those in non-European ancestries, and improve our understanding of the biology of OUD, we conducted a cross-ancestry meta-analysis using the Million Veteran Program (MVP). OUD cases in MVP had at least 1 International Classification of Diseases (ICD)-9 or ICD-10 code for opioid abuse or dependence (N=31,473). Opioid-exposed controls (N=394,471) had one or more outpatient opioid prescription fills. We conducted GWAS for each major ancestral group in MVP: African Americans (AAs; N=88,498), European Americans (EAs; N=302,585), and Hispanic Americans (HAs; N=34,861), followed by a cross-ancestry meta-analysis. Ten loci were GWS in the cross-ancestry meta-analysis, 8 of them novel. In addition to the known coding variant rs1799971 in OPRM1, which was the lead SNP genome-wide (p=6.78x10-10), and a recently reported exonic variant in FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1, and KCNN1. Ancestry-specific analyses identified an additional novel locus for each of the 3 ancestry groups. A supplementary meta-analysis within EAs that included MVP and other samples identified a locus in TSNARE1, which was also GWS in the cross-ancestry meta-analysis of all datasets. Gene-based association analyses identified 1 gene in AAs (CHRM2) and 3 in EAs (OPRM1, DRD2, and FTO). Significant genetic correlations (rg's) were identified for 127 traits, including positive correlations with schizophrenia, problematic alcohol use, and major depressive disorder. The most significantly enriched cell type group was the central nervous system with gene-expression enrichment identified in brain regions previously associated with substance use disorders. With a case sample 50% larger than that of the previous largest GWAS, we identified 14 loci for OUD, including 12 novel loci, some of which were ancestry specific. These findings increase our understanding of the biological pathways involved in OUD, which can inform preventive, diagnostic, and therapeutic efforts and thereby help to address the opioid epidemic.

Sign in / Sign up

Export Citation Format

Share Document